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TRANSCRIPT
Stefan Diewald, Andreas Möller, Luis Roalter
Technische Universität München, Distributed Multimodal Information
Processing Group, Arcisstr. 21, 80333 Munich, Germany,
e-mail: [email protected], [email protected], [email protected]
Tobias Stockinger, Marion Koelle, Patrick Lindemann, Matthias Kranz
University of Passau, Innstr. 43, 94032 Passau, Germany,
e-mail: <firstname.lastname>@uni-passau.de
Gamification-supported Exploration and
Practicing for Automotive User Interfaces and
Vehicle Functions
Stefan Diewald, Andreas Möller, Tobias Stockinger, Luis Roalter,
Marion Koelle, Patrick Lindemann and Matthias Kranz
Abstract When driving an unknown car, the interaction with its user
interfaces and the operation of (comfort) vehicle functions can be very
challenging and thus cause safety concerns. However, this problem
can be overcome already with a short learning and practicing phase.
For this reason, we analyze the potential of gamification for exploring
and practicing the use of automotive user interfaces and vehicle func-
tions. Based on the analysis of available examples, we have created a
gamified automotive exploration and practicing framework. The
framework allows exploring vehicle functions and user interfaces in
real vehicles as well as in applications for mobile devices. By reflect-
ing on the results of a first user study with the framework, we deliver
a set of guidelines for designing and evaluating gamified applications
for the automotive domain, which can serve as a support for future
developments.
2 Diewald et al.
1 Introduction
1.1 Motivation
People like gaming, winning, comparing, and sharing (Hsu and Lu
2004). This has been known for thousands of years and has been ex-
ploited in so-called serious games (Abt 2002) in many different areas
such as the military, academics, medicine, or professional training
(Zyda 2005). Serious games make use of the entertaining gaming ef-
fect to educate, train and inform their “players” (Michael and Chen
2005).
However, applications that are not framed in game scenarios can
likewise benefit from gamification. Especially with the success of the
location-based application Foursquare in 2010, which has made heavy
use of game design elements in its application, the research and design
community started to pay more attention to the so-called “gamifica-
tion” of non-gaming applications. Since then, the buzzword “gamifi-
cation” stands for the method for boosting the users’ motivation, com-
mitment, and participation. Deterding et al. researched the current use
of gamification and proposed the following definition: “Gamification
is the use of game design elements in non-game contexts” (Deterding
et al. 2011). However, a discussion about the term “gamification” has
recently emerged. Some researchers and game designers think that
many companies abused gamification by adding an independent
“game layer” to an existing application and/or by using extrinsic re-
wards to achieve short-term success. The use of game design elements
in non-game contexts with the goal of achieving long-term effects
based on intrinsic motivation is often referred to as “gameful design”
(Deterding et al. 2011). The term “gameful design” shall emphasize
the fact that game elements should be part of the concept already dur-
ing design and should not be added by an independent “gamification
layer.” Applications equipped with game design elements we call
“gamified” applications.
Since gamification can arouse sustainable motivation and strong
commitment, it has found its way into the automotive domain. Auto-
motive manufacturers are currently applying gamification approaches
3
for three prominent use cases: marketing (Tillström 2012), eco-driv-
ing (Inbar et al. 2011), and driving safety (Shi et al. 2012). While mar-
keting aims at convincing customers to buy a certain car, eco-driving
and driving safety applications are integrated in the vehicles’ infotain-
ment systems. These in-vehicle applications try to educate the drivers
by awarding points and badges for safe and ecological driving.
In our research, we investigate a new use case for gamification in
the automotive domain: exploration of automotive user interfaces and
practicing of vehicle functions. In a recent study1, it has been found
that cars ranging from compact to premium level are suffering from
user experience problems. Misplaced or too many controls, mislead-
ing labels, too deeply nested menus, and unreliable speech recognition
are demotivating the users. In addition to the non-self-explanatory in-
terfaces, users are often avoiding manuals for technical systems
(Novick and Ward 2006). These circumstances entail unsatisfied cus-
tomers that are able to use only a fraction of their (often expensively
bought) cars’ functions2. In addition, a study of the U.S. National
Highway Traffic Safety Administration (NHTSA) has revealed that
secondary and tertiary tasks in vehicles, such as adjusting the radio
and other devices integral to the vehicle, contribute to over 22 % of
all investigated crashes and near-crashes (NHTSA 2009). However,
it has been shown that many of these problems can be overcome by
practicing (Rouzikhah et al. 2013).
Therefore, we propose a gamification-supported framework for ex-
ploring and practicing automotive user interfaces and vehicle func-
tions. The framework consists of a mobile application that recreates
the vehicle cockpit for allowing offline exploration and training, and
an in-vehicle application that replaces the owner’s manual and pro-
vides hints and tips for the driver.
1 http://www.wiwo.de/technologie/auto/funktionen-im-auto-un-
sere-autos-sind-zu-schwer-zu-bedienen/7860276.html, last accessed
13 May 2013 2 http://www.wired.com/autopia/2013/04/car-tech-failing/, last ac-
cessed 12 May 2013
4 Diewald et al.
1.2 Contribution
In this chapter, we contribute a review of gamified applications in the
automotive domain and an overview over gamified learning environ-
ments. By examining the gathered examples, we point out potential
limitations and challenges of gamification on both considered areas.
Based on these findings, we contribute the concept and implemen-
tation of a gamified framework for exploration and practicing of au-
tomotive user interfaces and vehicle functions. Results of a first user
study and experiences gathered during the development are summa-
rized and serve as basis for some first guidelines that can support fu-
ture researches in designing and evaluating gamified automotive ap-
plications.
1.3 Chapter Overview
In Section 2, we first present the game design elements and game
mechanics that are commonly used in gamified systems. With this
knowledge at hand, we analyze existing gamified examples in differ-
ent automotive areas (Section 3) and briefly summarize important as-
pects of gamified learning applications (Section 4). In Section 5, the
results of the analyses of Sections 3 and 4 are summarized to chal-
lenges and limitations of gamification. Based on these findings, we
present the concept and implementation of our gamified framework
for exploring and practicing automotive user interfaces and vehicle
functions in Section 6. Parts of the framework have been evaluated in
a user study. The study and its results are summarized in Section 7. In
Section 8, we compile guidelines for future gamified automotive ap-
plication. The guidlines are based on our experiences from the devel-
opment of the framework and from our experiment with the frame-
work. Finally, we conclude by summarizing the presented ideas and
by giving an outlook to our future work (Section 9).
5
2 Elements and Mechanics of Gamification
In order to analyze the currently available applications, the basics of
gamification are summarized in this section. For creating a sustaina-
ble effect and lasting commitment, the source of motivation is im-
portant. Intrinsic motivation comes from the activity itself, whereas
extrinsic motivation comes from the outside (Deci et al. 1972). While
intrinsic motivation seems to be desirable, one also has to think about
the users that do not get intrinsic reward solely from the activity. In
that case, extrinsic rewards can substitute the missing initial intrinsic
motivation. However, there is the danger that by giving too much ex-
trinsic reward, the intrinsic reward diminishes (Deci et al. 2001) and
the person has to be kept in a reward loop forever (Zichermann and
Cunningham 2011, p. 27). In order to create intrinsic motivation, ac-
cording to McGonigal, four things need to be considered: satisfying
work (consisting of a clear goal and next actionable tasks), the
hope/experience of being successful, social connection, and meaning
(McGonigal 2011, p. 53). Satisfying work and the experience or hope
of being successful can be fulfilled by the characteristics of games
(McGonigal 2011, p. 29ff):
Goal: The sense of purpose. It focuses the users’ attention
and gives orientation.
Rules: Limitations on how the goal can be achieved. They
boost the users’ creativity, foster strategic thinking, and
help define the next actionable tasks.
Feedback system: How close is the user to the goal? (pro-
gress bar, points, levels)
Voluntary participation: Freedom to enter the game. Leads
to acceptance of rules and feedback.
The goal of social connection can be achieved by involving friends
via social networks or by teaming up people that have a common
unique goal. McGonigal claims that meaning can occur when users
are part of something “epic” (McGonigal 2011, p. 61ff). That means,
for example, that they can contribute to a superior goal that is carried
out and lasts for a longer time (e.g., fighting climate change). People
need something to master that adapts to their progress and their skills
(Zichermann and Cunningham 2011, p. 29). All of these factors make
6 Diewald et al.
up games and, as a result, they are important parts of gameful design.
In our analysis, we concentrate on game mechanics, since these are
the basic components of a game (Hunicke et al. 2004). According to
Zichermann and Cunningham (2011), the seven primary game me-
chanics are points, levels, leaderboards, badges, onboarding, chal-
lenges/quests, and engagement loops.
7
3 Gamification in the Automotive Domain
In this section, examples of automotive applications are examined.
The analysis is split up in two parts: applications outside vehicles and
applications for in-vehicle usage (Diewald et al. 2013). The analysis
focuses on applied gamification elements and includes a view on the
chosen type of motivation.
3.1 Gamified Automotive Applications outside Vehicles
3.1.1 Automotive Marketing with Gamified Applications
Outside vehicles, the main areas of application are marketing and
brand forming. By applying gamification, the automotive manufac-
turers want to create customers that are more attracted to their brands
and more profitable.
An example is Volkswagen’s BlueMotion Roulette3. In order to pro-
mote the lower fuel consumption of their new BlueMotion car,
Volkswagen created a game in which users could win the car by guess-
ing how far it can drive with one tank of fuel. However, instead of
creating a simple competition where the participants could enter their
guesses, they took a real car and drove along a selected road in Nor-
way. The route was visualized on Google Maps and the users could
bet via their Facebook account on a single road segment that had not
been taken by another player. On the competition day, the players
could follow the car’s journey live on the map and discuss it on Fa-
cebook. Since each user could only bet once, one could maximize
her/his chance of winning by finding out more about the car and its
fuel consumption before entering.
It can be assumed that for most users the possible extrinsic reward
of winning a car was the decisive factor for joining the “game”. How-
ever, the gaming experience caused by the roulette association, the
easy onboarding by presenting the facts about the car and the game in
a short simulation, and the challenge to beat other real players also
3 http://www.bluemotion.no/, last accessed May 29, 2013
8 Diewald et al.
caused intrinsic motivation for many players, which can be seen by
the large amount of Facebook likes and comments4.
Many applications reward users with badges etc. just for driving
around without having a clear goal. For example, the social driving
application Smileage5 rewards its users for meeting other vehicles that
are also using the Smileage application. Another example is MyFord
Mobile, which rewards its users, for instance, for driving 100,000
miles with an electric vehicle. The objective of such applications can
be seen in marketing, since its main purpose is sharing these badges
on different social networks.
3.1.2 Gamified Speed Monitoring Applications
The speed camera lottery6 was designed to reward people for doing
the right thing. Instead of just taking a picture of speeding cars, a mod-
ified traffic camera would photograph all passing cars. A portion of
the fines from the speeders would be pooled in a lottery in which each
of the law-obeying car owners would have a lottery ticket. A demo in
Stockholm lasting for three days resulted in a drop of the average
speed from 32 kilometers per hour to 25 kilometers per hour. In this
example, the motivation is mainly caused by the extrinsic reward,
which is the chance of winning the lottery. A deeper analysis of this
application is difficult, since there are no numbers for comparing the
effect against a standard traffic camera or for a longer period. How-
ever, it can be assumed that this gamified traffic camera could also
lead to undesired effects. For instance, more traffic could occur on the
road since people want to enter the lottery. Gamified road signs7,
which display friendly or unhappy smilies depending on whether the
speed limit is obeyed or not, are another example of applied gamifi-
cation. The effect of these signs is based on instantaneous feedback
and social pressure as all passersby can see the breach of rules.
4 https://www.facebook.com/BlueMotionRoulette, last accessed
May 29, 2013 5 http://smileage.vw.com/, last accessed June 5, 2013 6 http://wheels.blogs.nytimes.com/2010/11/30/speed-camera-lot-
tery-wins-vw-fun-theory-contest, last accessed May 30, 2013 7 http://www.smileysid.co.uk/, last accessed June 5, 2013
9
3.2 Gamified Automotive Applications in Vehicles
The following sections describe gamified applications that are in-
tended for use in real vehicles.
3.2.1 Navigation and Efficient Driving
A popular gamified application is the community-based traffic and
navigation mobile application Waze8. It rewards its users for mapping
uncharted areas and reporting traffic issues. Points and leaderboards
create a competition between users. However, these points are not
only used for comparing with other users, they are also used as a con-
fidence score for a user’s contribution. The top x percent of users are
further upgraded from Waze Grown-Ups to Waze Warriors, Waze
Knights, or Waze Royalties. The contribution to an active community
that has the goal to make driving more efficient partly creates an in-
trinsic motivation, which can cause users to diverge from their route
to join in9
The I-GEAR (incentives and gaming environments for automobile
routing) project aims at changing users’ behavior in order to reduce
traffic congestion (McCall and Koenig 2012). For example, users
could be rewarded for taking a later bus or going to a suburb shopping
mall instead of the one in the city center with free bus tickets or dis-
counts at a store in the selected suburb mall. In addition to the imme-
diate rewards, users also would get points for sticking to the applica-
tion’s recommendations. These points could be converted into
material rewards later. Drivers could also team up and gather points
to win prizes like free car insurance for one year when their team has
the highest score at the end of the year. This project sets a lot on ex-
trinsic rewards.
3.2.2 Safe Driving
The mobile application Driving Miss Daisy by Shi et al. (2012) per-
forms a gamified driving style assessment. Instead of just showing a
8 http://www.waze.com/, last accessed June 4, 2013 9 http://www.technologyreview.com/news/422583/social-surveil-
lance-yields-smarter-directions/page/2/, last accessed May 28, 2013
10 Diewald et al.
score of points, the performance is evaluated by a virtual passenger
on the backseat (‘Miss Daisy’) who cheers or whimpers depending on
the driving performance. In addition, a game summary is presented at
the end of a drive. Besides the instantaneous feedback over thumbs-
up and thumbs-down, the driver can earn virtual money on each drive,
which is accumulated over multiple rounds for comparison with other
players. The application has several levels of difficulty which are in-
creased based on the former performance. The performance of a drive
can be compared to historical drives on the same route of the player
him/herself (self-competition), and with the performance of other
players (public competition).
CleverMiles10 is based on an external device that has to be con-
nected with the vehicle’s on-board diagnostics II (OBD-II) port. The
device logs and analyzes the driving, and when safe driving is de-
tected, the user gets CleverPoints that can be redeemed against prod-
ucts from different partners. In order to improve the players’ driving,
the system displays driving style recommendations. The application
further allows users to share the driving performance data with Face-
book friends and other drivers. Since the application is still in closed
beta-trial, no information about the effectiveness is available so far.
3.2.3 Eco-driving
Gamified eco-driving applications can be found in many cars. An ex-
ample is Ford’s SmartGauge with EcoGuide11, which was developed
for hybrid vehicles. It informs the user about the current state and ef-
ficiency level of the vehicle’s drive. When the car is driven at the most
efficient level, “efficiency leaves” are growing on the right part of the
dashboard as a reward for the user. Other examples are the color
switching eco-gauge of the Chevrolet Volt, or Kia’s ECOdynamics
system12 which offers different setups that challenge the driver to get
the best economy rating. With Fiat’s eco:Drive13, drivers can analyze
10 http://www.clevermiles.com/, last accessed May 20, 2013 11 http://stanfordbusiness.tumblr.com/post/32317645424/why-
gamification-is-really-powerful, last accessed May 24, 2013 12 http://thenextweb.com/shareables/2012/09/22/can-kias-gamifi-
cation-change-way-drive-cars/, last accessed May 24, 2013 13 http://www2.fiat.co.uk/ecodrive/, last accessed August 19, 2013
11
their eco-driving-related behavior in real-time or afterwards at home.
In addition to a score in form of an eco:Index, drivers can earn
eco:Badges and contribute with their savings to create a better virtual
place called eco:Ville.
The examined eco-driving applications challenge the users in a
very emotional way (Tractinsky et al. 2011): Efficient eco-driving is
indicated by green colors or by flourishing nature. In less efficient
conditions, the displays are changing to the colors yellow or red and
the leaves are disappearing. Thus, the user gets the feeling that some-
thing is broken or the vehicle is being mistreated. Competitive eco-
driving can create a very strong intrinsic motivation. According to
Deterding14, the gamified EcoChallenge application by Ecker et
al. (2011) was so motivating that users would even go through red
lights, which is an unintended behavior.
14 http://en.slideshare.net/dings/pawned-gamification-and-its-dis-
contents, slide 41, last accessed June 5, 2013
12 Diewald et al.
4 Gamified Learning and Exploration
The second pillar of our automotive training framework is gamified
learning and exploration. Since there are several examples and an ex-
tensive theoretical background analysis of gamified learning and
training environments in Chapters “EDUCATION” and “From Mar-
ket Place to Collusion Detection: Case Studies of Gamification in Ed-
ucation”, we concentrate in our overview on the basics and only pre-
sent a few examples that were considered in the conception phase of
our proposed framework.
4.1 Gamified Learning
Already in the 1980s, Malone conducted experiments to find out what
makes computer games fun and how this can be used for instructional
computer games (Malone 1980). A thorough literature review on the
positive impacts of gaming in learning, skill enhancement, and en-
gagement settings has been presented by Connolly et al. (2013). Their
review revealed that gamified learning application and serious games
could boost knowledge acquisition, content understanding as well as
increase the learner’s affection and motivation. However, they also
point out that the learning effectiveness is not automatically optimized
by integrating game mechanics in learning applications.
When analyzing current examples of gamified learning environ-
ments (Muntean 2011; Simões et al. 2013), the use of game elements
does not directly optimize the learning efficacy, but has mainly an
impact on the learners’ motivation (Domínguez et al. 2013). This is
also an important factor for our framework, since our goal is getting
the drivers to explore the user interface of the car and practice the
usage of other vehicle functions as early as possible and best before
the first drive with an unknown vehicle.
4.2 Gamified Tutorials, Training and Exploration
Gamification is also applied in tutorials for online services and com-
puter applications. For example, the online cloud storage service
13
Dropbox15 offers a tutorial mode that visualizes the users’ ‘learning’
progress and rewards them with 250 MB extra space when completing
the tutorial. Additional space can be gained by completing other dif-
ferent tasks on a task list with progress display. By using the space as
extrinsic reward, the service lets its users take over advertising on so-
cial networks etc.
GamiCAD is a gamified interactive tutorial system for first time
AutoCAD users (Li et al. 2012). A comparison of the gamified tutorial
with the default in-product interactive tutorial system revealed that
users of the gamified version showed higher subjective engagement
levels and completed test tasks 20 % to 76 % faster. Ribbon Hero 216
is a game for learning Microsoft Office. The interactive tutorial con-
sists of game setting challenges, which expose students step-by-step
to more Office features. Students are encouraged to explore and learn
on their own through points that are awarded for using basic functions
as well as new functions that can be unlocked by completing chal-
lenges. Another motivation is the score sharing functions that allows
publishing the current score via social media.
Orientation Passport by Fitz-Walter et al. (2011) is an example of
a gamified mobile exploration application. The smartphone applica-
tion is targeted at new students during their university orientation
phase. It provides a digital orientation schedule of important student
events accompanied with other helpful tools, such as an interactive
campus map, a contact list, or a service information page. By check-
ing into events, adding people to the contact list or answering ques-
tions to university services, the new students can unlock a maximum
of 20 achievements. The results from a pilot study show that the
achievement system motivated students to visit events and explore the
campus and its services. However, downsides of the gamification
were that some users only visited places once for unlocking an
achievement and added random people as “friends” to their contact
list to get the respective badge.
In order to enforce or train certain behaviors, aspects of behavioral
economics can be combined with game elements. An example is the
mobile application SmartPiggy (Stockinger et al. 2013). In this app,
15 https://www.dropbox.com/getspace, last accessed September 02,
2013 16 http://www.ribbonhero.com/, last accessed August 19, 2013
14 Diewald et al.
color-coded progress bars and badges support the task of saving
money. In contrast to other implementations that only award badges
to users, this application makes use of people’s loss aversion and takes
away gained badges when users fail to reach their goals.
Gamification cannot only persuade end-users to explore a system
or application, but can also be used to explore the use and spread of
technology. An example is the mobile application NFC Heroes
(Kranz et al. 2013). The app is set in a trading card context and awards
users with gadgets and points for documenting Near-Field Communi-
cation (NFC) technologies in their environment. The gathered data is
used by researchers to explore the use of NFC and to measure the
adoption of this technology.
Gamification is not only used for exploration and tutorials but also
for different kinds of personal training. Example areas of mobile per-
sonal training applications are mobile fitness coaches (Kranz et al.
2013; McCallum 2012) or training applications for teaching methods
in (higher) education (Möller et al. 2011). Especially for training ap-
plications, it is important to match the way of information presenta-
tion with the target audience. Expert users may not be willing to “play
through a game” in order to access the information they are looking
for.
15
5 Potential Limitations and Challenges of Gamification
Looking at the examined examples, some challenges, and limitations
of gameful design can be derived:
Games are voluntary and have no serious consequences: All of
the applications examined here fulfil the voluntary nature. However,
when gamification approaches areas such as electronic road pric-
ing (Merugu et al. 2009), the voluntary nature could be limited when
the driver has to either take part in the game or stay out of the game
area. The competitive eco-driving example showed that the serious-
ness of traffic regulations could be surpassed by the intrinsic motiva-
tion coming from the gaming character. Applications that can have an
influence on the driving style should be analyzed and extensively
tested before they are released or integrated into vehicles.
Games abstract and simplify complex processes for a better
gaming experience: In order to have a clearer relationship between
the actions and the goal, games often simplify complex processes.
However, when gamifying a real process, the precision and accuracy
has to meet the requirements of the process. For example, a safe driv-
ing assessment application that only rewards the user based on rules
like “drive slowly and do not brake” would not meet the requirements
of safe participation in road traffic.
Games live from instant and unambiguous feedback: To en-
courage desired behavior, immediate and unambiguous feedback is
important. However, during driving it can be very difficult to clearly
present feedback without distracting the driver from the driving task.
Although little icons in the dashboard or audio feedback could reduce
the distraction, these could be ambiguous so the driver might know to
have achieved something without knowing exactly what was
achieved. A solution could be to shift the detailed explanation of the
achievement to the next stop (e.g., at red lights).
How to phase out extrinsic rewards: When the motivation of a
gamified application is mainly based on extrinsic rewards, it can be a
difficult process to phase out these rewards. An approach could be to
draw the “player’s” attention to the intrinsic rewards one gets from
using the application (e.g., focus on the social connection, the mas-
16 Diewald et al.
tered challenges, or the learning progress). At the same time, the ex-
trinsic rewards, which perhaps helped to attract the user, could be
gradually reduced. The use and height of extrinsic rewards should be
looked at in detail during the testing phase. The reward should not
exceed a certain value that motivates users to execute unnecessary,
rash, and unsafe driving maneuvers.
17
6 Gamification-based Framework for Automotive User Interface
Training
6.1 Purpose of the Framework
In order to investigate the potential of gamification for exploring au-
tomotive user interfaces and practicing the use of (comfort) vehicle
functions, we developed a prototypical framework. We had the fol-
lowing research questions (RQs) in mind during the conception phase
of the framework:
RQ1: Does gamification have an influence on the training
motivation of the subjects?
RQ2: Does gamification during the training phase have an
influence on the driving performance?
RQ3: Does gamification influence the acceptance of recom-
mendations given by a training system?
RQ4: Will subjects perform safety-critical actions or even
follow dangerous recommendations while driving in order to
get a higher score from the framework?
While RQ1 (effects on motivation), RQ2 (effects on driving per-
formance) and RQ3 (effect on acceptance) focus on positive aspects
of gamification, RQ4 is intended to unveil possible negative gamifi-
cation effects (see also Chapter NEGATIVE ASPECTS). The focus
of our research is on the effect of gamification on the actual driving
performance.
6.2 General Functionality
The framework is split up into two gamification-supported explora-
tion and practicing modes (see Fig. 1). The first approach is the online
mode that is running directly on the car’s in-vehicle infotainment
(IVI) system. Similar to classical step-by-step tutorials, the ‘tutorial
18 Diewald et al.
and quiz mode’ guides the driver through the most important func-
tions and awards points and badges with ongoing progress. Examples
of trained functions are e.g. (1) adjusting the seat, (2) activating the
hazard warning lights, (3) activating the adaptive cruise control, or
(4) changing the radio station. The different learning units are inter-
rupted by randomly selected quiz questions to rehearse already
learned functions. By answering the questions within a certain time
limit (in a safe driving situation and vehicular context, e.g. while the
car is parked on private property), the user can earn bonus points. In
contrast to the ‘tutorial and quiz mode’, which is only available when
the car is parked, the ‘background mode’ is monitoring the driving
while the car is moving. Whenever the user performs a secondary or
tertiary task (Kern and Schmidt 2009), i.e. a task not directly related
to driving, the driving behavior is analyzed in order to estimate the
driver’s distraction (for algorithms cf. Alonso et al. (2012)). At the
end of the drive, the application calculates a score where 100 % means
that the driver is using the human machine interface (HMI) without
noticeable distraction and 0 % means that a high amount of distraction
was detected for each performed secondary and tertiary task. As a re-
sult, the application suggests the driver what should be further prac-
ticed, and – when available – it suggests less distracting control alter-
natives, e.g. using the steering wheel volume control instead of the
radio’s volume control, or controlling a function via the voice com-
mand system. This score is also saved in a high score list.
Fig. 1 The gamified exploration supports two modes: The online mode is running on the car’s
infotainment system, the offline mode is a mobile application for exploring the human
machine interface (HMI) independent from the vehicle.
Online Mode(in car)
Tutorial and Quiz Mode
Background Mode
Offline Mode(mobile app)
Exploration Mode
Quiz Mode
19
a. The main menu of the mobile application (offline mode). The cockpit mode al-
lows free exploration of the vehicle’s recreated cockpit (see Fig. 2 b).
b. The cockpit mode of the mobile application. By clicking on an interactive ele-
ment in the cockpit, the application shows usage details (see Fig. 2 c) and awards
points to the user for newly found functions.
c. The details view explains the usage of the different interactive elements. There is
also a walk-through for the vehicles infotainment menu. The example in this figure
shows details on the wiper stalk switch.
Fig. 2 The three figures depict example screens of the mobile application that rep-
resents the offline mode of our proposed framework.
20 Diewald et al.
The in-car mode is complemented by the offline mode that is real-
ized as a mobile application for smartphones and tablet PCs. The ‘ex-
ploration mode’ allows exploring the human-machine interface. For
newly identified functions, entries from a ‘to explore’ list are ticked
off. In the ‘quiz mode’, random questions have to be answered by the
user and points are awarded. When a set of questions on a special
topic (e.g. the navigation system) has been successfully completed, an
‘expert badge’ is awarded to the user.
Both modes have been prototypically implemented. The online
mode is implemented on a driving simulator based on a real car cock-
pit (see Fig. 3). By analyzing messages on the CAN (Controller Area
Network) bus, the Java-based application can log the use of the car
functions. In addition, the online mode also controls parts of the dash-
board and the display on top of the center stack, which allows showing
the feedback and the tutorial instructions directly on the car’s built-in
displays. The offline mode has been realized as mobile application for
the Android platform. In both applications, the gamification elements
can be turned off. This allows analyzing motivation and learning ef-
fects caused by gamification.
Fig. 3 The driving simulator used for the evaluation of the in-vehicle mode (online
mode) of the framework. It is the real cockpit of a BMW 5-series. The framework
controls parts of the dashboard and the display on top of the center stack, and can
log most controls by monitoring the vehicle’s CAN bus.
21
6.3 Sample Scenarios for the Gamified Automotive Training
Framework
6.3.1 Interactive Tutorial for Car Buyers
The proposed framework can be used in different scenarios. An ex-
ample is its usage as an interactive tutorial for car buyers. The explo-
ration and tutorial functionality of the online mode can be used as a
replacement for the owner’s manual. It could be automatically started
before the first drive with the new car and later on started on demand
whenever the driver needs help or wants to explore unknown control
elements or menu points.
The offline mode could be interesting for buyers that are waiting for
the delivery of their ordered car. Using the mobile application distrib-
uted by the car manufacturer could increase the buyer’s excited antic-
ipation and ensure that one knows how to use the car’s functions right
from the beginning.
6.3.2 Guidance for Rental Car and Car Sharing Users
Especially when driving an unknown vehicle – as is often the case
with rental cars and car sharing vehicles – drivers can be overtaxed
by the operation of tertiary car functions (Kern and Schmidt 2009).
This could, for example, be overcome with a gamified preset mode
that automatically starts when the driver enters the car. The online
mode could be a virtual guide that shows the driver what one can ad-
just before the drive in order to have a less stressful drive. This could
contain things like seat and mirrors adjustments, choosing the desired
radio station, or setting the temperature of the air conditioning. Be-
sides the intrinsic motivation of having a more convenient drive, pos-
sible extrinsic rewards could be the reduction of the insurance deduct-
ible or free car sharing minutes.
22 Diewald et al.
7 Evaluation of the Offline Mode Prototype
In a first test, the offline mode prototype has been evaluated in order
to answer the research questions presented in Section 6.1.
7.1 Evaluation Setting and Methodology
For evaluating the effects of the offline mode on the driving perfor-
mance and vehicle function handling, subjects had to perform given
secondary and tertiary tasks while driving in a simulator (see Fig. 3).
In order to measure the effect of the gamified training application,
the participants were randomly divided into two groups (between-
subjects design):
(1) Without any training (control group).
(2) 10 minutes training with the offline mode (experiment group).
The metrics were time to task completion, lane deviation, subjec-
tive perceived workload, and ratings on a questionnaire. The per-
ceived workload was measured by the NASA Task Load Index
(NASA-TLX) questionnaire. The additional questionnaire asked
about previous knowledge of the subjects and let them rate statements
concerning their motivation as well as their perception of the game-
fulness of the overall experiment.
7.1.1 Tasks
The driving task was the so-called Lane Change Task17 by Daim-
ler (Harbluk et al. 2007). The maximum speed was set to 60 km/h.
The secondary or tertiary tasks (operating tasks) to be performed by
the subjects were shown on the lower part of the dashboard and were
triggered automatically based on the driven distance. The subjects
were instructed to focus on their speed, to perform the lane changes
indicated by the simulation tool, and to keep their track. Although the
participants should focus on driving safety, the displayed operating
tasks should be performed as fast as possible. The operating tasks are
summarized in Table 1.
17 http://sunburst.usd.edu/~schieber/ppt/MATTES2003-
powerpoint.pdf, last accessed September 23, 2013
23
Task
No.
Task Description Start
Distance
End
Distance
T1 Increase radio volume via steering
wheel control 400 m 600 m
T2 Change radio station via steering
wheel controls 800 m 1000 m
T3 Play CD: Sheryl Crow 1200 m 1800 m
T4 Activate the Active Cruise Control 2000 m 2400 m
T5 Start Navigation to ‘Home’ 2600 m 3300 m
Table 1 Overview of secondary and tertiary tasks users had to operate during the
drive. The tasks were displayed on the lower part of the dashboard and were trig-
gered automatically based on the driven distance. The task instruction was active
from start distance to end distance and was hidden when the task was fulfilled.
The experiment began with a brief introduction for both groups. In a
pre-experiment questionnaire, demographic data, driving experience
and experience with technical systems such as smartphones were
gathered.
Afterwards, the experiment group got a short introduction to the
mobile application prototype (offline mode). Then, the subjects could
freely explore and use the gamified application for a maximum of
10 minutes. The subjects in the control group immediately progressed
with the driving task.
The driving task consisted of four laps (each around 3300 m,
~ 3.5 minutes) in the Lane Change Task (LCT) simulation. In the first
lap, subjects got an introduction to the simulation environment and to
the Lane Change Task. In the second lap, ground truth data on the
driving performance was recorded. During ground truth, no extra op-
erating tasks had to be performed. For the last two laps, subjects had
to perform the additional operating tasks (cf. Table 1) in parallel to
the normal driving task. After the third lap, a summary of their oper-
ating performance in form of an automatically calculated score (com-
posed of accomplished task score and time bonus) was presented to
the subjects. Before they started the fourth lap, the experimenter told
the subjects that the score is rather low and they could get into a high-
score list when they perform the operation tasks faster and more ac-
curate in the next lap. After each lap, subjects had to do a subjective
assessment of their mental workload with the NASA Task Load Index
questionnaire.
24 Diewald et al.
After the driving experiment, the subjects filled in a post-experi-
ment questionnaire. The questionnaire included questions on the driv-
ing and operating performance. The experiment group further an-
swered questions on the tested gamified mobile application.
7.1.2 Participants
For the first test, we recruited 30 subjects between 19 and 28 years
(median = 25 years, standard deviation σ = 2.53). There were 5 female
and 25 male participants. Most of the participants were students or
research assistants. The average experiment duration was 35 minutes.
Subjects received a direct compensation for their participation in form
of a 5 € gift card for an online retailer. The average driving experience
was 6.0 years (σ = 2.53). The subjects were randomly assigned to the
experiment and control group. A Student’s t-test (α = 0.05, two-tail)
on the driving experiences of the control and the experiment group
showed no significant difference (P(T ≤ t) = 0.069). In addition, there
were no significant differences in experience with and interest in tech-
nical devices between both groups.
7.2 Results
7.2.1 Results of Driving Experiment
The presentation of the results focuses on the parts relevant for
providing answers to our research questions.
The analysis of the LCT track deviation data gave the following
results. In comparison to the second lap (ground truth) without addi-
tional operating tasks, the lane deviation increased for the control
group on average about 48.7 % (σ = 0.43) for the third lap and 39.6 %
(σ = 0.46) for the fourth lap. The experiment group had slightly better
results. Their lane deviation increased by 42.1 % (σ = 0.27) for the
third lap and 23.7 % (σ = 0.27) for the fourth lap. However, no signif-
icant differences could be found between the results for both laps
(lap 1: P(T ≤ t) = 0.65, lap 2: P(T ≤ t) = 0.26).
25
Task
Lap 1 Lap 2
Control
n = 15
Exp.
n = 15
Control
n = 14
Exp.
n = 14
Task 1: Increase volume
via steering wheel 93.3 % 93.3 % 100 % 100 %
Task 2: Change radio sta-
tion via steering wheel 80.0 % 73.3 % 78.6 % 78.6 %
Task 3: Play CD: Sheryl
Crow 80.0 % 73.3 % 100 % 92.9 %
Task 4: Activate Active
Cruise Control 33.3 % 60.0 % 35.7 % 71.4 %
Task 5: Start Navigation
to ‘Home’ 73.3 % 66.7 % 100 % 78.6 %
Table 2 Accomplishment rates for operating tasks. There were 15 participants in
both groups for the first lap. For the second lap, in each group one subject decided
to end the driving experiment early.
The task completion rates were almost equal for both groups (see
Table 2). The only significant difference can be seen for task 4. The
completion rate for the active cruise control task is twice as high for
the experiment group as for the control group.
The results from the subjective assessment of the mental workload
with the NASA-TLX (weighted score from 0 to 100) correlate with
the average lane deviation of the LCT. No significant difference was
found between the groups. For the second lap (ground truth without
operating task), an average NASA-TLX score of 24.7 (σ = 13.9) was
calculated. The third lap (first experiment lap with operating tasks)
had an average score of 57.0 (σ = 21.3), the fourth lap resulted in an
average score of 39.4 (σ = 18.4). The average NASA-TLX scores of
all users split up into categories are depicted in Fig. 4.
In addition, the subjects rated statements on the driving experiment
on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree).
The results are summarized in Table 3. The goal was to measure
whether the usage of the mobile application changes the perception of
the driving task. However, no significant differences between both
groups could be observed.
26 Diewald et al.
Figure 4 Average NASA-TLX scores for all subjects. The subject could assess the
different categories on a scale from 0 to 20 with 0 = very low and 20 = very high.
0
5
10
15
20
Mental demand Physical demand Temporal demand
Average NASA-TLX scores
2nd lap (ground truth) 3rd lap 4th lap
0
5
10
15
20
Performance Effort Frustration
2nd lap (ground truth) 3rd lap 4th lap
27
Statement Control Group Exp. Group
Mean σ Mean σ
The operating tasks were
too difficult for me. 2.07 0.59 1.87 0.74
My goal was to drive
safely. 3.27 1.03 3.67 0.98
My goal was to accom-
plish the tasks quickly. 4.34 0.62 4.27 0.70
My goal was to reach a
high score. 4.07 1.03 4.20 0.77
The experiment felt more
like a game for me. 3.47 0.92 3.34 1.05
Table 3 Mean and standard deviation (σ) of rated statements concerning the driving
experiment. The statements had to be rated on a 5-point Likert scale with
1 = ‘strongly disagree’ and 5 = ‘strongly agree’. No significant differences between
the control group and the experiment group can be observed.
7.2.2 Results related to the Mobile Application
The subjects in the experiment group (n = 15) further rated statements
on the mobile application on a 5-point Likert scale (1 = strongly dis-
agree, 5 = strongly agree). The fun factor of the application was rated
with an average score of 4.20 (σ = 0.56). The usefulness of the appli-
cation was confirmed with an average rating of 4.27 (σ = 0.46). The
subjects can further think of using such an application for unknown
cars (mean = 3.80, σ = 0.77). Participants further thought that the use
of the application made the operating tasks easier during the driving
experiment (mean = 4.47, σ = 0.52). Regarding the motivation, the
subjects stated with an average score of 4.73 (σ = 0.46) that the quiz
mode with the ability to make a high score motivated them to improve
their initial score.
28 Diewald et al.
7.3 Discussion
Based on the results from the performed experiment, answers to the
research questions shall be provided in the discussion of the results.
7.3.1 Influence of Gamification on the Training Motivation (RQ1)
Subjects in the experiment group clearly stated that the possibility to
improve their score in the quiz mode of the mobile application was a
good incentive to perform the quiz several times. However, so far the
quiz mode only asks for the location of input elements. For that rea-
son, the quiz can quickly become boring. Subjects suggested to im-
plement different categories of questions or to ask multiple-choice
questions on the operating elements and their functions. In addition,
the idea of unlocking new application features by exploring the virtual
cockpit was appealing.
The individual statements of the subjects match with the rating of
the application presented in Section 7.2.1. In summary, game ele-
ments had a positive influence on the training motivation. However,
users need the feeling that the quiz evolves with their growing exper-
tise.
7.3.2 Influence of Gamification on the Driving Performance (RQ2)
Although the subjects in the experiment group stated in the post-ques-
tionnaire that the use of the gamified mobile application had helped
them during the driving experiment, no significant difference in the
LCT results could be observed compared to the results of the control
group (see Section 7.2.1). That means that the training application had
no direct influence on the driving performance.
For the operation tasks, the results were comparable for both
groups. The only significant difference was in task 4, which was the
activation of the active cruise control. This was the only function in
our operation task set that is not yet widely available and had to be
operated through a small lever located behind the steering wheel. This
indicates that the mobile training application is beneficial for func-
tions that are not yet common in cars and/or are not plainly visible.
29
7.3.3 Influence of Gamification on Recommendations (RQ3)
Subjects stated in the final interviews that the mobile application had
both informative and game character. Especially the cockpit view and
the function list have been seen as an information source. The quiz
mode was rated to be more like a ‘learning game.’ However, when we
observed the users interacting with the mobile application, we noticed
that the informative character faded into the background. Most sub-
jects tapped systematically or completely randomly on the virtual
cockpit in order to find all functions. When a function was found, the
description was often just quickly scanned and possible recommenda-
tions or usage hints were overlooked. When we mentioned this in the
interview, the subjects stated that their goal was to activate the quiz
mode quickly.
7.3.4 Negative Aspects of Gamification (RQ4)
One negative aspect of gamification was already mentioned in Sec-
tion 7.3.3. Instead of reading the information, subjects tried to keep
the game flowing. A solution could be to implement a short compul-
sory break that allows the user to read the text. Another idea is to cut
down the amount of information presented at a time. Alternatively,
the textual explanation of functions could be enhanced with interac-
tive graphics, video snippets, or audio.
For evaluating the game element ‘score’ and, thus, ‘competition,’ a
score was computed during the driving experiment and displayed to
the subjects after completing a lap. We further intensified the ‘com-
petition’ after the first lap by saying that they can enter a high-score
list when they get more points in the second lap. From the values in
Table 3, it can be seen that on average the subjects concentrated more
on the operating tasks and their score than on driving safely. Although
both groups had only slightly the feeling that the experiment is more
like a game (see Table 3), they disregarded the instruction that the
main objective was to drive safely. When we asked the subjects why
they had concentrated on the score, they mainly named the competi-
tion as decisive factor. The high-score list influenced even subjects
who stated in the pre-experiment questionnaire not to be very com-
petitive.
30 Diewald et al.
8 Towards Guidelines for Gamification in the Automotive
Domain
The experiences gathered during the development and the first labor-
atory test with the framework could serve as a basis for future gami-
fied automotive applications as well as their evaluation. The follow-
ing statements summarize our findings:
Abstraction can be dangerous, details also: Games often abstract
complex tasks in order to offer a better game flow experience (see
Section 5). However, especially when gamifying real processes that
could cause safety issues, the precision and accuracy has to meet the
requirements of the process. For example, in a first version, we only
delivered a very short and simplified description for the adaptive
cruise control stalk switch, which lead to situation where subjects did
not know how the set speed of the ACC could be reset. Some subjects
activated the adaptive cruise control and the car in the simulation ac-
celerated automatically to more than double the predetermined speed.
However, also too much details can be dangerous during driving. Fol-
lowing the rule of immediate feedback (cf. Section 5), we created dif-
ferent icons with a short reward text that were shown on the dashboard
of the simulator as soon as a task was solved. The message was only
shown when the car drove straight and at a constant speed. However,
some participants found these reward messages so interesting that
they partially ignored the primary driving task they should focus on.
Therefore, when designing a gamified application one has to find a
balance for the right degree of detail and abstraction and how unam-
biguous feedback can be provided.
Make game rules clear: In our first experiments, we tried to hide
the game mechanics, and did not offer an explanation on how points
or awards can be earned. However, as soon as the subjects found out
that they got points for a certain action, they repeated this action as
often as possible to get as many points as possible. This led to drastic
performance drops as the subjects’ focus was on finding out the rules
for getting more points. As already stated in Section 2, rules are im-
portant parts of games and need to be clear for the “play-
ers” (McGonigal 2011, p. 29ff).
31
Test in a non-gaming context: The first iterations of the frame-
work prototype were tested with a computer steering wheel in front of
two large displays on a desk. The driving simulation software was
similar to Geoquake’s 3D Google Maps simulation18. Already during
the first test rounds, we noticed that most participants had the feeling
that the situation was unreal and more like a game. For that reason,
we changed to the realistic car cockpit simulator afterwards (see
Fig. 3). This hugely changed the perception of the subjects (see Ta-
ble 3). On the software side, we changed from our own satellite im-
age-based simulation tool to the established Lane Change Task. Its
analysis tool further allows measuring the lane deviation (see Fig. 4)
and comparing the subjects’ driving performance when performing
secondary or tertiary tasks with their baseline driving when they only
concentrate on the driving. In our experience, one can only find out
the caused gaming effect of a gamified application, when it is tested
in a non-gaming context, i.e. with a realistic driving simulation or a
real car. In that way, it can be found out whether the gamified system
has an influence on the seriousness of the driving task.
Do not gamify the task of driving: An important aspect we no-
ticed during the experiment was that a gamified automotive applica-
tion should not gamify the task of driving. For example, at an early
stage, our framework suggested to use a different input modality for
18 http://geoquake.jp/en/webgame/DrivingSimulatorPerspective/,
last accessed September 27, 2013.
Fig. 4 The figure presents an example output of the track analysis tool of the Lance
Change Task (LCT). The solid black line depicts the ideal track; the dotted line
indicates the track driven by a subject during a task. By calculating the area between
the two lines, the quality of the track keeping can be analyzed. This allows drawing
conclusions on the subjects’ distraction.
32 Diewald et al.
controlling a function when it noticed that the driver left the lane dur-
ing an operation task, and awarded points when the driver success-
fully used the suggested modality (Diewald et al. 2012). Although the
application chose the drivers’ preferred modality, the subjects mainly
concentrated on gaining points and the driving performance drasti-
cally decreased.
Our results further show that competition is a very motivating fac-
tor for users to lose focus from the primary driving task (see Sec-
tion 7.3.4). This coincides also with the observations of Deterding
presented in Section 3.2.2, which means that competition for safety
critical applications should be avoided.
The most important experiences from the tests during the develop-
ment however were that the concept should be developed iteratively
and that after each slight change of the game mechanics a test is nec-
essary. Even the change from one game element to another can be
critical and needs to be evaluated thoroughly.
33
9 Conclusion and Future Work
In this chapter, we first looked at common game elements and me-
chanics and then analyzed several examples of gamified applications
in the automotive domain and for learning environments. Based on
these analyses, we brought out limitations and challenges of gamifi-
cation in the automotive domain, which were considered when creat-
ing our proposed gamified framework for exploring automotive user
interfaces and practicing vehicle functions. After presenting the con-
cept and implementation of this framework, we summarized the re-
sults from our first study. Based on these results and experiences dur-
ing the development, we formulated some guidelines that we share in
order to support further research on gamification in the automotive
domain.
We are currently enhancing the mobile application and conducting
an experiment with the online mode of the framework. We will inves-
tigate whether the training effect with a recreated virtual car interface
(offline mode) is comparable to the effect when training with a real
car interface (online mode). The comparison of the offline mode and
the online mode will also be used to determine whether gamification
in the real car distorts the perception of the seriousness of ‘driving a
real car’.
Although gamification also has several negative aspects that need
to be considered, we are convinced that our approach could improve
the situation for people who often switch cars, but want to have stress-
free and comfortable rides.
Acknowledgments We gratefully acknowledge the assistance in research and im-
plementation of our student Duc Ha Minh and thank the Institute for Human-Ma-
chine Communication at Technische Universität München for letting us use their
driving simulator.
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